These are my solutions to project euler problems in Python. My main goal was to obtain the solution as fast as possible, whatever means necessary (i.e. get the solution first, then worry about code structure and layout). I would record the times from when I started the problem, to when I had the solution. These are listed in time.csv. I would also record the runtime of the code.
I'm now working on refactoring the solutions and factoring out common functions and code to identify the underlying functions I used in many solutions.
problems/ is a mirror of the problems on the website (in case it ever goes down)
##Analyse data!
import pandas as pd
df = pd.read_table('time.csv',sep=',')
# wall time grows with brute number
df[df.brute_number.notnull()].plot('brute_number','wall_time',kind='scatter',logx=True,logy=True)
# difficulty vs solve time
sdf = df.groupby('difficulty').median()
sdf['difficulty'] = sdf.index.tolist()
sdf.plot('difficulty','to_complete',kind='scatter')
# problem_num vs solve time
df.plot('num','to_complete',kind='scatter')
df.plot('num','difficulty',kind='scatter')
# hours spent solving and waiting for run to finish
df.to_complete.sum()/60
To profile programs:
`python -m cProfile <script>`
To RunSnakeRun bottleneck analyze
`python -m cProfile -o <script_name.out> <script>`
`sudo python /Users/dominicspadacene/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/runsnakerun/runsnake.py <script_name.out>`
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